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The paper studied the cluster processing and efficiency on electrocardiogram waveform. 24 hours dynamic electrocardiogram waveforms were the analysis object. Analysis vectors were produced by wavelet transform, by which the feature of electrocardiogram was extracted. Four cluster methods were selected and compared in the paper on clustering efficiency and time complexity. According to the experiments result, on self-organization map (SOM) neural network, the basic waveform recognizing rate reached 77%, and accuracy rate reached 85%. It is the best in performance among others. On time complexity comparison SOM neural work will be limited in processing more object, it can be substituted by classification method that studied in other paper. [ABSTRACT FROM AUTHOR]

Acquisition of large scale good quality training samples is becoming a major issue in machine learning based human motion analysis. This paper presents a method to simulate continuous gross human body motion with the intention to establish a human motion corpus for learning and recognition. The simulation is achieved by a temporal-spatialtemporal decomposition of human motion into actions, joint actions and actionlets based on the human kinematic model. The actionlet models the primitive moving phase of a joint and represents the muscle movement governed by kinesiological principles. Joint actions and body actions are constructed from actionlets through constrained concatenation and synchronization. Methods for concatenation and synchronization are proposed in this paper. An action corpus with small number of action vocabularies is created to verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]